Personalization of Web Search Results Using Term, Category, and Link-Based User Profiles

ABSTRACT

A system and method for creating a user profile and for using the user profile to order search results returned by a search engine. The user profile is based on search queries submitted by a user, the user&#39;s specific interaction with the documents identified by the search engine and personal information provided by the user. Terms for the user profile may be selected from the documents accessed by the user by performing paragraph sampling or context analysis. Generic scores associated with the search results are modulated by the user profile to measure their relevance to a user&#39;s preference and interest. The search results are re-ordered accordingly so that the most relevant results appear on the top of the list. User profiles can be created and/or stored on the client side or server side of a client-server network environment.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/676,711, filed Sep. 30, 2003, entitled “Personalization of WebSearch,” which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to the field of a search enginein a computer network system, in particular to system and method ofcreating a user profile for a user of a search engine and using the userprofile to customize search results in response to search queriessubmitted by the user.

BACKGROUND OF THE INVENTION

Search engines provide a powerful source of indexed documents from theInternet (or an intranet) that can be rapidly scanned in response to asearch query submitted by a user. Such a query is usually very short (onaverage about two to three words). As the number of documents accessiblevia the Internet grows, the number of documents that match the query mayalso increase. However, not every document matching the query is equallyimportant from the user's perspective. As a result, a user is easilyoverwhelmed by an enormous number of documents returned by a searchengine, if the engine does not order the search results based on theirrelevance to the user's query.

One approach to improving the relevance of search results to a searchquery is to use the link structure of different web pages to computeglobal “importance” scores that can be used to influence the ranking ofsearch results. This is sometimes referred to as the PageRank algorithm.A more detailed description of the PageRank algorithm can be found inthe article “The Anatomy of a Large-Scale Hypertextual Search Engine” byS. Brin and L. Page, 7^(th) International World Wide Web Conference,Brisbane, Australia and U.S. Pat. No. 6,285,999, both of which arehereby incorporated by reference as background information.

An important assumption in the PageRank algorithm is that there is a“random surfer” who starts his web surfing journey at a randomly pickedweb page and keeps clicking on the links embedded in the web pages,never hitting the “back” button. Eventually, when this random surfergets bored of the journey, he may re-start a new journey by randomlypicking another web page. The probability that the random surfer visits(i.e., views or downloads) a web page depends on the web page's pagerank.

From an end user's perspective, a search engine using the PageRankalgorithm treats a search query the same way no matter who submits thequery, because the search engine does not ask the user to provide anyinformation that can uniquely identify the user. The only factor thataffects the search results is the search query itself, e.g., how manyterms are in the query and in what order. The search results are a bestfit for the interest of an abstract user, the “random surfer”, and theyare not be adjusted to fit a specific user's preferences or interests.

In reality, a user like the random surfer never exists. Every user hashis own preferences when he submits a query to a search engine. Thequality of the search results returned by the engine has to be evaluatedby its users' satisfaction. When a user's preferences can be welldefined by the query itself, or when the user's preference is similar tothe random surfer's preference with respect to a specific query, theuser is more likely to be satisfied with the search results. However, ifthe user's preference is significantly biased by some personal factorsthat are not clearly reflected in a search query itself, or if theuser's preference is quite different from the random user's preference,the search results from the same search engine may be less useful to theuser, if not useless.

As suggested above, the journey of the random surfer tends to be randomand neutral, without any obvious inclination towards a particulardirection. When a search engine returns only a handful of search resultsthat match a query, the order of the returned results is lesssignificant because the requesting user may be able to afford the timeto browse each of them to discover the items most relevant to himself.However, with billions of web pages connected to the Internet, a searchengine often returns hundreds or even thousands of documents that matcha search query. In this case, the ordering of the search results is veryimportant. A user who has a preference different from that of the randomsurfer may not find what he is looking for in the first five to tendocuments listed in the search results. When that happens, the user isusually left with two options: (1) either spending the time required toreview more of the listed documents so as to locate the relevantdocuments; or (2) refining the search query so as to reduce the numberof documents that match the query. Query refinement is often anon-trivial task, sometimes requiring more knowledge of the subject ormore expertise with search engines than the user possesses, andsometimes requiring more time and effort than the user is willing toexpend.

For example, assume that a user submits to a search engine a searchquery having only one term “blackberry”. Without any other context, onthe top of a list of documents returned by a PageRank-based searchengine may be a link to www.blackberry.net, because this web page hasthe highest page rank. However, if the query requester is a person withinterests in foods and cooking, it would be more useful to order thesearch results so as to include at the top of the returned results webpages with recipes or other food related text, pictures or the like. Itwould be desirable to have a search engine that is able to reorder itssearch results, or to otherwise customize the search results, so as toemphasize web pages that are most likely to be of interest to the personsubmitting the search query. Further, it would be desirable for such asystem to require minimal input from individual users, operating largelyor completely without explicit input from the user with regard to theuser's preferences and interests. Finally, it would be desirable forsuch a system to meet users' requirements with respect to security andprivacy.

SUMMARY

A search engine utilizes user profiles to customize search results. Auser profile comprises multiple items that characterize a user's searchpreference. These items are extracted from various information sources,including previous search queries submitted by the user, links from orto the documents identified by the previous queries, sampled contentfrom the identified documents as well as personal information implicitlyor explicitly provided by the user.

When the search engine receives a search query from a user, it firstidentifies a set of documents that match the search query. Each documentis associated with a generic rank based on the document's page rank, thetext associated with the document, and the search query. The searchengine also identifies the user's profile and correlates the userprofile with each of the identified documents. The correlation between adocument and the user profile produces a profile rank for the document,indicating the relevance of the document to the user. The search enginethen combines the document's generic rank and profile rank into apersonalized rank. Finally, the documents are ordered according to theirpersonalized ranks.

In one embodiment, a user profile may comprise a plurality ofsub-profiles, each sub-profile characterizing the user's interest from adifferent perspective. A term-based profile comprises a plurality ofterms, each term carrying a weight indicative of its importance relativeto other terms. A category-based profile comprises multiple categories,optionally organized into a hierarchical map. The user's searchpreferences may be associated with at least a subset of the multiplecategories, each category having an associated weight indicating theuser's interest in the documents falling into this category. There maybe multiple category-based profiles for a user. In some embodiments, thesub-profiles include a link-based profile, which includes a plurality oflinks that are, directly or indirectly, related to identified documents,each link having a weight indicating the importance of the link. Linksin the link-based profile may be further organized with respect todifferent hosts and domains.

The present invention, including user profile construction and searchresults re-ordering and/or scoring, can be implemented on either theclient side or the server side of a client-server network environment.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned features and advantages of the invention as well asadditional features and advantages thereof will be more clearlyunderstood hereinafter as a result of a detailed description ofpreferred embodiments of the invention when taken in conjunction withthe drawings.

FIG. 1 illustrates a client-server network environment.

FIG. 2 illustrates multiple sources of user information and theirrelationship to a user profile.

FIG. 3 is an exemplary data structure that may be used for storingterm-based profiles for a plurality of users.

FIG. 4A is an exemplary category map that may be used for classifying auser's past search experience.

FIG. 4B is an exemplary data structure that may be used for storingcategory-based profiles for a plurality of users.

FIG. 5 is an exemplary data structure that may be used for storinglink-based profiles for a plurality of users.

FIG. 6 is a flowchart illustrating paragraph sampling.

FIG. 7A is a flowchart illustrating context analysis.

FIG. 7B depicts a process of identifying important terms using contextanalysis.

FIG. 8 illustrates a plurality of exemplary data structures that may beused for storing information about documents after term-based,category-based and/or link-based analyses, respectively.

FIG. 9A is a flowchart illustrating a personalized web search processaccording to one embodiment.

FIG. 9B is a flowchart illustrating a personalized web search processaccording to another embodiment.

FIG. 10 is a block diagram of a personalized search engine.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DESCRIPTION OF EMBODIMENTS

The embodiments discussed below include systems and methods that createa user profile based a user's past experience with a search engine andthen use the user profile to rank search results in response to searchqueries provided by the user.

FIG. 1 provides an overview of a typical client-server networkenvironment 100 in which the present invention may be implemented. Aplurality of clients 102 are connected to a search engine system 107through a network 105, e.g., the Internet. Search engine system 107comprises one or more search engines 104. A search engine 104 isresponsible for processing a search query submitted by a client 102,generating search results in accordance with the search query andreturning the results to the client. Search engine system 107 may alsocomprise one or more content servers 106 and one or more user profileservers 108. A content server 106 stores a large number of indexeddocuments retrieved from different websites. Alternately, or inaddition, the content server 106 stores an index of documents stored onvarious websites. In one embodiment, each indexed document is assigned apage rank according to the document's link structure. The page rankserves as a query independent measure of the document's importance. Asearch engine 104 communicates with one or more content servers 106 toselect a plurality of documents in response to a specific search query.The search engine assigns a score to each document based on thedocument's page rank, the text associated with the document, and thesearch query.

A user profile server 108 stores a plurality of user profiles. Eachprofile includes information that uniquely identifies a user as well ashis previous search experience and personal information, which can beused to refine search results in response to the search queriessubmitted by this user. Different approaches are available for userprofile construction. For example, a user profile can be created byrequiring a first-time user to fill in a form or answer a survey. Thisapproach may be useful in certain applications such as opening a bankaccount. But it is hardly a favorable one in the context of a searchengine. First, a user's interaction with a search engine is usually adynamic process. As time goes on, the user's interests may change. Thischange may be reflected by the search queries submitted by the user, orby the user's handling of the search results, or both. The user'sanswers to questions on a form tend to become less useful over time,unless the user chooses to update his answers periodically. Unlike anoccasional update of phone number in the case of an on-line bankaccount, frequent updates of a user profile in the case of a searchengine significantly affect its user friendliness, which is an importantconsideration when a user chooses among the search engines currentlyavailable. Further, it is known that users are reluctant to provideexplicit feedback, such as filling out of a form, as many users find ittoo burdensome. Thus, while some users may provide explicit feedback ontheir interests, it is desirable to have a procedure for implicitlyobtaining information about the user's interests without requiring anyexplicit or new actions by the user.

It is has been observed that a search engine user's past searchactivities provide useful hints about the user's personal searchpreferences. FIG. 2 provides a list of sources of user information thatare beneficial for user profile construction. For example, previouslysubmitted search queries 201 are very helpful in profiling a user'sinterests. If a user has submitted multiple search queries related todiabetes, it is more likely than not that this is a topic of interest tothe user. If the user subsequently submits a query including the term“organic food”, it can be reasonably inferred that he may be moreinterested in those organic foods that are helpful in fighting diabetes.Similarly, the universal resource locators (URL) 203 associated with thesearch results in response to the previous search queries and theircorresponding anchor texts 205, especially for search result items thathave been selected or “visited” by the user (e.g., downloaded orotherwise viewed by the user), are helpful in determining the user'spreferences. When a first page contains a link to a second page, and thelink has text associated with it (e.g., text neighboring the link), thetext associated with the link is called “anchor text” with respect tothe second page. Anchor text establishes a relationship between the textassociated with a URL link in a document and another document to whichthe URL link points. The advantages of anchor text include that it oftenprovides an accurate description of the document to which the URL linkpoints, and it can be used to index documents that cannot be indexed bya text-based search engine, such as images or databases.

After receiving search results, the user may click on some of the URLlinks, thereby downloading the documents referenced by those links, soas to learn more details about those documents. Certain types of generalinformation 207 can be associated with a set of user selected or useidentified documents. For purposes of forming a user profile, theidentified documents from which information is derived for inclusion inthe user profile may include: documents identified by search resultsfrom the search engine, documents accessed (e.g., viewed or downloaded,for example using a browser application) by the user (includingdocuments not identified in prior search results), documents linked tothe documents identified by search results from the search engine, anddocuments linked to the documents accessed by the user, or any subset ofsuch documents.

The general information 207 about the identified documents may answerquestions such as, what is the format of the document? Is it inhypertext markup language (HTML), plain text, portable document format(PDF), or Microsoft Word? What is the topic of the document? Is it aboutscience, health or business? This information is also helpful inprofiling the user's interests. In addition, information about a user'sactivities 209 with respect to the user selected documents (sometimesherein call the identified documents), such as how long the user spentviewing the document, the amount of scrolling activity on the document,and whether the user has printed, saved or bookmarked the document, alsosuggests the importance of the document to the user as well as theuser's preferences. In some embodiments, information about useractivities 209 is used both when weighting the importance of informationextracted or derived from the user identified documents. In someembodiments, information about user activities 209 is used to determinewhich of the user identified documents to use as the basis for derivingthe user profile. For example, information 209 may be used to selectonly documents that received significant user activity (in accordancewith predefined criteria) for generating the user profile, orinformation 209 may be used to exclude from the profiling processdocuments that the user viewed for less than a predefined thresholdamount of time.

Finally, the content of the identified documents from previous searchactivities is a rich source of information about a user's interests andpreferences. Key terms appearing in the identified documents and theirfrequencies with which they appear in the identified documents are notonly useful for indexing the document, but are also a strong indicationof the user's personal interests, especially when they are combined withother types of user information discussed above. In one embodiment,instead of the whole documents, sampled content 211 from the identifieddocuments is extracted for the purpose of user profile construction, tosave storage space and computational cost. In another embodiment,various information related to the identified documents may beclassified to constitute category information 213 about the identifieddocuments. More discussion about content sampling, the process ofidentifying key terms in an identified document and the usage of thecategory information is provided below.

Optionally, a user may choose to offer personal information 215,including demographic and geographic information associated with theuser, such as the user's age or age range, educational level or range,income level or range, language preferences, marital status, geographiclocation (e.g., the city, state and country in which the user resides,and possibly also including additional information such as streetaddress, zip code, and telephone area code), cultural background orpreferences, or any subset of these. Compared with other types ofpersonal information such as a user's favorite sports or movies that areoften time varying, this personal information is more static and moredifficult to infer from the user's search queries and search results,but may be crucial in correctly interpreting certain queries submittedby the user. For example, if a user submits a query containing “Japaneserestaurant”, it is very likely that he may be searching for a localJapanese restaurant for dinner. Without knowing the user's geographicallocation, it is hard to order the search results so as to bring to thetop those items that are most relevant to the user's true intention. Incertain cases, however, it is possible to infer this information. Forexample, users often select results associated with a specific regioncorresponding to where they live.

Creating a user profile 230 from the various sources of user informationis a dynamic and complex process. In some embodiments, the process isdivided into sub-processes. Each sub-process produces one type of userprofile characterizing a user's interests or preferences from aparticular perspective. They are:

-   -   a term-based profile 231—this profile represents a user's search        preferences with a plurality of terms, where each term is given        a weight indicating the importance of the term to the user;    -   a category-based profile 233—this profile correlates a user's        search preferences with a set of categories, which may be        organized in a hierarchal fashion, with each category being        given a weight indicating the extent of correlation between the        user's search preferences and the category; and    -   a link-based profile 235—this profile identifies a plurality of        links that are directly or indirectly related to the user's        search preferences, with each link being given a weight        indicating the relevance between the user's search preferences        and the link.

In some embodiments, the user profile 230 includes only a subset ofthese profiles 231, 233, 235, for example just one or two of theseprofiles. In one embodiment, the user profile 230 includes a term-basedprofile 231 and a category-based profile 233, but not a link-basedprofile 235.

In one embodiment, a user profile is created and stored on a server(e.g., user profile server 108) associated with a search engine. Theadvantage of such deployment is that the user profile can be easilyaccessed by multiple computers, and that since the profile is stored ona server associated with (or part of) the search engine 104, it can beeasily used by the search engine 104 to personalize the search results.In another embodiment, the user profile can be created and stored on theuser's computer, sometimes called the client in a network environment.Creating and storing a user profile on a user's computer not onlyreduces the computational and storage cost for the search engine'sservers, but also satisfies some users' privacy requirements. In yetanother embodiment, the user profile may be created and updated on theclient, but stored on a search engine server. Such embodiment combinessome of the benefits illustrated in the other two embodiments. Adisadvantage of this arrangement is that it may increase the networktraffic between clients and the search engine servers. It is understoodby a person of ordinary skill in the art that the user profiles of thepresent invention can be implemented using client computers, servercomputers, or both.

FIG. 3 illustrates an exemplary data structure, a term-based profiletable 300, that may be used for storing term-based profiles for aplurality of users. Table 300 includes a plurality of records 310, eachrecord corresponding to a user's term-based profile. A term-basedprofile record 310 includes a plurality of columns including a USER_IDcolumn 320 and multiple columns of (TERM, WEIGHT) pairs 340. The USER_IDcolumn stores a value that uniquely identifies a user or a group ofusers sharing the same set of (TERM, WEIGHT) pairs, and each (TERM,WEIGHT) pair 340 includes a term, typically 1-3 words long, that isusually important to the user or the group of users and a weightassociated with the term that quantifies the importance of the term. Inone embodiment, the term may be represented as one or more n-grams. Ann-gram is defined as a sequence of n tokens, where the tokens may bewords. For example, the phrase “search engine” is an n-gram of length 2,and the word “search” is an n-gram of length 1.

N-grams can be used to represent textual objects as vectors. This makesit possible to apply geometric, statistical and other mathematicaltechniques, which are well defined for vectors, but not for objects ingeneral. In the present invention, n-grams can be used to define asimilarity measure between two terms based on the application of amathematical function to the vector representations of the terms.

The weight of a term is not necessarily a positive value. If a term hasa negative weight, it may suggest that the user prefers that his searchresults should not include this term and the magnitude of the negativeweight indicates the strength of the user's preference for avoiding thisterm in the search results. By way of example, for a group of surfingfans at Santa Cruz, Calif., the term-based profile may include termslike “surfing club”, “surfing event” and “Santa Cruz” with positiveweights. The terms like “Internet surfing” or “web surfing” may also beincluded in the profile. However, these terms are more likely to receivea negative weight since they are irrelevant and confusing with theauthentic preference of the users sharing this term-based profile.

A term-based profile itemizes a user's preference using specific terms,each term having certain weight. If a document matches a term in auser's term-based profile, i.e., its content includes exactly this term,the term's weight will be assigned to the document; however, if adocument does not match a term exactly, it will not receive any weightassociated with this term. Such a requirement of relevance between adocument and a user profile sometimes may be less flexible when dealingwith various scenarios in which a fuzzy relevance between a user'spreference and a document exists. For example, if a user's term-basedprofile includes terms like “Mozilla” and “browser”, a documentcontaining no such terms, but other terms like “Galeon” or “Opera” willnot receive any weight because they do not match any existing term inthe profile, even though they are actually Internet browsers. To addressthe need for matching a user's interests without exact term matching, auser's profile may include a category-based profile.

FIG. 4A illustrates a hierarchal category map 400 according to the OpenDirectory Project (http://dmoz.org/). Starting from the root level ofmap 400, documents are organized under several major topics, such as“Art”, “News”, “Sports”, etc. These major topics are often too broad todelineate a user's specific interest. Therefore, they are furtherdivided into sub-topics that are more specific. For example, topic “Art”may comprise sub-topics like “Movie”, “Music” and “Literature” and thesub-topic “Music” may further comprise sub-sub-topics like “Lyrics”,“News” and “Reviews”. Note that each topic is associated with a uniqueCATEGORY_ID like 1.1 for “Art”, 1.4.2.3 for “Talk Show” and 1.6.1 for“Basketball”.

A user's specific interests may be associated with multiple categoriesat various levels, each of which may have a weight indicating the degreeof relevance between the category and the user's interest. In oneembodiment, a category-based profile may be implemented using a Hashtable data structure as shown in FIG. 4B. A category-based profile table450 includes a table that comprises a plurality of records 341-1, 342-2,. . . 342-n, each record including a USER_ID and a pointer pointing toanother data structure, such as a table, e.g., one of the tables shownon the right side of FIG. 4B. This table may include two columns, aCATEGORY_ID column and a WEIGHT column. The CATEGORY_ID column containsa category's identification number as shown in FIG. 4A, suggesting thatthis category is relevant to the user's interests and the value in theWEIGHT column indicates the degree of relevance of the category to theuser's interests.

A user profile based upon the category map 400 is a topic-orientedimplementation. The items in a category-based profile can also beorganized in other ways. In one embodiment, a user's preference can becategorized based on the formats of the documents identified by theuser, such as HTML, plain text, PDF, Microsoft Word, etc. Differentformats may have different weights. In another embodiment, a user'spreference can be categorized according to the types of the identifieddocuments, e.g., an organization's homepage, a person's homepage, aresearch paper, or a news group posting, each type having an associatedweight. Another type category that can be used to characterize a user'ssearch preferences is document origin, for instance the countryassociated with each document's host. In yet another embodiment, theabove-identified category-based profiles may co-exist, with each onereflecting one aspect of a user's preferences.

Besides term-based and category-based profiles, another type of userprofile is referred to as a link-based profile. As discussed above, thePageRank algorithm is based on the link structure that connects variousdocuments over the Internet. A document that has more links pointing toit is often assigned a higher page rank and therefore attracts moreattention from a search engine. Link information related to a documentidentified by a user can also be used to infer the user's preferences.In one embodiment, a list of preferred URLs are identified for a user byanalyzing the frequency of his access to those URLs. Each preferred URLmay be further weighted according to the time spent by the user and theuser's scrolling activity at the URL, and/or other user activities (209,FIG. 2) when visiting the document at the URL. In another embodiment, alist of preferred hosts are identified for a user by analyzing theuser's frequency of accessing web pages of different hosts. When twopreferred URLs are related to the same host the weights of the two URLsmay be combined to determine a weight for the host. In anotherembodiment, a list of preferred domains are identified for a user byanalyzing the user's frequency of accessing web pages of differentdomains. For example, for finance.yahoo.com, the host is“finance.yahoo.com” while the domain is “yahoo.com”.

FIG. 5 illustrates a link-based profile using a Hash table datastructure. A link-based profile table 500 includes a table 510 thatincludes a plurality of records 520, each record including a USER_ID anda pointer pointing to another data structure, such as table 510-1. Table510-1 may include two columns, LINK_ID column 530 and WEIGHT column 540.The identification number stored in the LINK_ID column 530 may beassociated with a preferred URL or host. The actual URL/host/domain maybe stored in the table instead of the LINK_ID, however it is preferableto store the LINK_ID to save storage space.

A preferred list of URLs and/or hosts includes URLs and/or hosts thathave been directly identified by the user. The preferred list of URLsand/or host may furthermore extend to URLs and/or hosts indirectlyidentified by using methods such as collaborative filtering orbibliometric analysis, which are known to persons of ordinary skill inthe art. In one embodiment, the indirectly identified URLs and/or hostinclude URLs or hosts that have links to/from the directly identifiedURLs and/or hosts. These indirectly identified URLs and/or hosts areweighted by the distance between them and the associated URLs or hoststhat are directly identified by the user. For example, when a directlyidentified URL or host has a weight of 1, URLs or hosts that are onelink away may have a weight of 0.5, URLs or hosts that are two linksaway may have a weight of 0.25, etc. This procedure can be furtherrefined by reducing the weight of links that are not related to thetopic of the original URL or host, e.g., links to copyright pages or webbrowser software that can be used to view the documents associated withthe user selected URL or host. Irrelevant Links can be identified basedon their context or their distribution. For example, copyright linksoften use specific terms (e.g., copyright or “All rights reserved” arecommonly used terms in the anchor text of a copyright link); and linksto a website from many unrelated websites may suggest that this websiteis not topically related (e.g., links to the Internet Explorer websiteare often included in unrelated websites). The indirect links can alsobe classified according to a set of topics and links with very differenttopics may be excluded or be assigned a low weight.

The three types of user profiles discussed above are generallycomplimentary to one another since different profiles delineate a user'sinterests and preferences from different vantage points. However, thisdoes not mean that one type of user profile, e.g., category-basedprofile, is incapable of playing a role that is typically played byanother type of user profile. By way of example, a preferred URL or hostin a link-based profile is often associated with a specific topic, e.g.,finance.yahoo.com is a URL focusing on financial news. Therefore, whatis achieved by a link-based profile that comprises a list of preferredURLs or hosts to characterize a user's preference may also beachievable, at least in part, by a category-based profile that has a setof categories that cover the same topics covered by preferred URLs orhosts.

It is a non-trivial operation to construct various types of userprofiles that can be stored in the data structures shown in FIGS. 3-5based on the user information listed in FIG. 2. Given a documentidentified (e.g., viewed) by a user, different terms in the document mayhave different importance in revealing the topic of the document. Someterms, e.g., the document's title, may be extremely important, whileother terms may have little importance. For example, many documentscontain navigational links, copyright statements, disclaimers and othertext that may not be related to the topic of the document. How toefficiently select appropriate documents, content from those documentsand terms from within the content is a challenging topic incomputational linguistics. Additionally, it is preferred to minimize thevolume of user information processed, so as make the process of userprofile construction computationally efficient. Skipping less importantterms in a document helps in accurately matching a document with auser's interest.

Paragraph sampling (described below with reference to FIG. 6) is aprocedure for automatically extracting content from a document that maybe relevant to a user. An important observation behind this procedure isthat less relevant content in a document, such as navigational links,copyright statements, disclaimer, etc., tend to be relatively shortsegments of text. In one embodiment, paragraph sampling looks for theparagraphs of greatest length in a document, processing the paragraphsin order of decreasing length until the length of a paragraph is below apredefined threshold. The paragraph sampling procedure optionallyselects up to a certain maximum amount of content from each processedparagraph. If few paragraphs of suitable length are found in a document,the procedure falls back to extracting text from other parts of thedocument, such as anchor text and ALT tags.

FIG. 6 is a flowchart illustrating the major steps of paragraphsampling. Paragraph sampling begins with the step 610 of removingpredefined items, such as comments, JavaScript and style sheets, etc.,from a document. These items are removed because they are usuallyrelated to visual aspects of the document when rendered on a browser andare unlikely to be relevant to the document's topic. Following that, theprocedure may select the first N words (or M sentences) at step 620 fromeach paragraph whose length is greater than a threshold value,MinParagraphLength, as sampled content. In one embodiment, the values ofN and M are chosen to be 100 and 5, respectively. Other values may beused in other embodiments.

In order to reduce the computational and storage load associated withthe paragraph sampling procedure, the procedure may impose a maximumlimit, e.g., 1000 words, on the sampled content from each document. Inone embodiment, the paragraph sampling procedure first organizes all theparagraphs in a document in length decreasing order, and then starts thesampling process with a paragraph of maximum length. It is noted thatthe beginning and end of a paragraph depend on the appearance of theparagraph in a browser, not on the presence of uninterrupted a textstring in the HTML representation of the paragraph. For this reason,certain HTML commands, such as commands for inline links and for boldtext, are ignored when determining paragraph boundaries. In someembodiments, the paragraph sampling procedure screens the first N words(or M sentences) so as to filter out those sentences includingboilerplate terms like “Terms of Service” or “Best viewed”, because suchsentences are usually deemed irrelevant to the document's topic.

Before sampling a paragraph whose length is above the threshold value,the procedure may stop sampling content from the document if the numberof words in the sampled content has reached the maximum word limit. Ifthe maximum word limit has not been reached after processing allparagraphs of length greater than the threshold, optional steps 630,640, 650 and 670 are performed. In particular, the procedure adds thedocument title (630), the non-inline HREF links (640), the ALT tags(650) and the meta tags (670) to the sampled content until it reachesthe maximum word limit.

Once the documents identified by a user have been scanned, the sampledcontent can be used for identifying a list of most important (orunimportant) terms through context analysis. Context analysis attemptsto learn context terms that predict the most important (or unimportant)terms in a set of identified documents. Specifically, it looks forprefix patterns, postfix patterns, and a combination of both. Forexample, an expression “x's home page” may identify the term “x” as animportant term for a user and therefore the postfix pattern “* homepage” can be used to predict the location of an important term in adocument, where the asterisk “*” represents any term that fits thispostfix pattern. In general, the patterns identified by context analysisusually consist of m terms before an important (or unimportant) term andn terms after the important (or unimportant) term, where both m and nare greater than or equal to 0 and at least one of them is greater than0. Typically, m and n are less than 5, and when non-zero are preferablybetween 1 and 3. Depending on its appearance frequency, a pattern mayhave an associated weight that indicates how important (or unimportant)the term recognized by the pattern is expected to be.

According to one embodiment of the present invention (FIG. 7A), contextanalysis has two distinct phases, a training phase 701 and anoperational phase 703. The training phase 701 receives and utilizes alist of predefined important terms 712, an optional list of predefinedunimportant terms 714, and a set of training documents (step 710). Insome embodiments, the list of predefined unimportant terms is not used.The source of the lists 712, 714 is not critical. In some embodiments,these lists 712, 714 are generated by extracting words or terms from aset of documents (e.g., a set of several thousand web pages of high pagerank) in accordance with a set of rules, and then editing them to removeterms that in the opinion of the editor do not belong in the lists. Thesource of the training documents is also not critical. In someembodiments, the training documents comprise a randomly orpseudo-randomly selected set of documents already known to the searchengine. In other embodiments, the training documents are selected from adatabase of documents in the search engine in accordance with predefinedcriteria.

During the training phase 701, the training documents are processed(step 720), using the lists of predefined important and unimportantterms, so as to identify a plurality of context patterns (e.g., prefixpatterns, postfix patterns, and prefix-postfix patterns) and toassociate a weight with each identified context pattern. During theoperational phase 703, the context patterns are applied to documentsidentified by the user (step 730) to identify a set of important terms(step 740) that characterize the user's specific interests andpreferences. Learning and delineating a user's interests and preferencesis usually an ongoing process. Therefore, the operational phase 703 maybe repeated to update the set of important terms that have been capturedpreviously. This may be done each time a user accesses a document,according to a predetermined schedule, at times determined in accordancewith specified criteria, or otherwise from time to time. Similarly, thetraining phase 701 may also be repeated to discover new sets of contextpatterns and to recalibrate the weights associated with the identifiedcontext patterns.

Below is a segment of pseudo code that exemplifies the training phase:

For each document in the set {   For each important term in the document{    For m = 0 to MaxPrefix {      For n = 0 to MaxPostfix {      Extract the m words before the important       term and the nwords after the important       term as s;       Add 1 toImportantContext(m,n,s);      }    }   }   For each unimportant term inthe document {    For m = 0 to MaxPrefix {      For n = 0 to MaxPostfix{       Extract the m words before the       unimportant term and the nwords after       the unimportant term as s;       Add 1 toUnimportantContext(m,n,s);      }    }   } } For m = 0 to MaxPrefix {  For n = 0 to MaxPostfix {    For each value of s {      Set the weightfor s to a function of      ImportantContext(m,n,s), and     UnimportantContext(m,n,s);    }   } }

In the pseudo code above, the expression s refers to a prefix pattern(n=0), a postfix pattern (m=0) or a combination of both (m>0 & n>0).Each occurrence of a specific pattern is registered at one of the twomulti-dimensional arrays, ImportantContext(m,n,s) orUnimportantContext(m,n,s). The weight of a prefix, postfix orcombination pattern is set higher if this pattern identifies moreimportant terms and fewer unimportant terms and vice versa. Note that itis possible that a same pattern may be associated with both importantand unimportant terms. For example, the postfix expression “* operatingsystem” may be used in the training documents 716 in conjunction withterms in the list of predefined important terms 712 and also used inconjunction with terms in the list of predefined unimportant terms 714.In this situation, the weight associated with the postfix pattern “*operating system” (represented by the expression Weight(1.0,“operatingsystem”)) will take into account the number of times the postfixexpression is used in conjunction with terms in the list of predefinedimportant terms as well as the number of times the postfix expression isused in conjunction with terms in the list of predefined unimportantterms. One possible formula to determine the weight of a context patterns is:

Weight(m,n,s)=Log(ImportantContext(m,n,s)+1)−Log(UnimportantContext(m,n,s)+1).

Other weight determination formulas may be used in other embodiments.

In the second phase of the context analysis process, the weightedcontext patterns are used to identify important terms in one or moredocuments identified by the user. Referring to FIG. 7B, in the firstphase a computer system receives training data 750 and creates a set ofcontext patterns 760, each context pattern having an associated weight.The computer system then applies the set of context patterns 760 to adocument 780. In FIG. 7B, previously identified context patterns foundwithin the document 780 are highlighted. Terms 790 associated with thecontext patterns are identified and each such term receives a weightbased on the weights associated with the context patterns. For example,the term “Foobar” appears in the document twice, in association with twodifferent patterns, the prefix pattern “Welcome to *” and the postfixpattern “* builds”, and the weight 1.2 assigned to “Foobar” is the sumof the two patterns' weights, 0.7 and 0.5. The other identified term“cars” has a weight of 0.8 because the matching prefix pattern “world'sbest *” has a weight of 0.8. In some embodiments the weight for eachterm is computed using a log transform, where the final weight is equalto log(initial weight+1). It is possible that the two terms “Foobar” and“cars” may not be in the training data 750 and may have never beenencountered by the user before. Nevertheless, the context analysismethod described above identifies these terms and adds them to theuser's term-based profile. Thus, context analysis can be used todiscover terms associated with a user's interests and preferences evenwhen those terms are not included in a predefined database of terms.

As noted, the output of context analysis can be used directly inconstructing a user's term-based profile. Additionally, it may be usefulin building other types of user profiles, such as a user'scategory-based profile. For example, a set of weighted terms can beanalyzed and classified into a plurality of categories coveringdifferent topics, and those categories can be added to a user'scategory-based profile.

After executing the context analysis on a set of documents identified byor for a user, the resulting set of terms and weights may occupy alarger amount of storage than allocated for each user's term-basedprofile. Also, the set of terms and corresponding weights may includesome terms with weights much, much smaller than other terms within theset. Therefore, in some embodiments, at the conclusion of the contextanalysis, the set of terms and weights is pruned by removing termshaving the lowest weights (A) so that the total amount of storageoccupied by the term-based profile meets predefined limits, and/or (B)so as to remove terms whose weights are so low, or terms that correspondto older items, as defined by predefined criteria, that the terms aredeemed to be not indicative of the user's search preferences andinterests. In some embodiments, similar pruning criteria and techniquesare also applied to the category-based profile and/or the link-basedprofile.

In some embodiments, a user's profile is updated each time the userperforms a search and selects at least one document from the searchresults to download or view. In some embodiments, the search enginebuilds a list of documents identified by the user (e.g., by selectingthe documents from search results) over time, and at predefined times(e.g., when the list reaches a predefined length, or a predefined amountof time has elapsed), performs a profile update. When performing anupdate, new profile data is generated, and the new profile data ismerged with the previously generated profile data for the user. In someembodiments, the new profile data is assigned higher importance than thepreviously generated profile data, thereby enabling the system toquickly adjust a user's profile in accordance with changes in the user'ssearch preferences and interests. For example, the weights of items inthe previously generated profile data may be automatically scaleddownward prior to merging with the new profile data. In one embodiment,there is a date associated with each item in the profile, and theinformation in the profile is weighted based on its age, with olderitems receiving a lower weight than when they were new. In otherembodiments, the new profile data is not assigned high importance thanthe previously generated profile data.

The paragraph sampling and context analysis methods may be usedindependently or in combination. When used in combination, the output ofthe paragraph sampling is used as input to the context analysis method.

It is further noted that the above-described methods used for creatinguser profiles, e.g., paragraph sampling and context analysis, may bealso leveraged for determining the relevance of a candidate document toa user's preference. Indeed, the primary mission of a search engine isto identify a series of documents that are most relevant to a user'spreference based on the search queries submitted by the user as well asthe user's user profile. FIG. 8 illustrates several exemplary datastructures that can be used to store information about a document'srelevance to a user profile from multiple perspectives. For eachcandidate document, each identified by a respective DOC_ID, term-baseddocument information table 810 includes multiple pairs of terms andtheir weights, category-based document information table 830 includes aplurality of categories and associated weights, and link-based documentinformation table 850 includes a set of links and corresponding weights.

The rightmost column of each of the three tables (810, 830 and 850)stores the rank (i.e., a computed score) of a document when the documentis evaluated using one specific type of user profile. A user profilerank can be determined by combining the weights of the items associatedwith a document. For instance, a category-based or topic-based profilerank may be computed as follows. A user may prefer documents aboutscience with a weight of 0.6, while he dislikes documents about businesswith a weight of −0.2. Thus, when a science document matches a searchquery, it will be weighted higher than a business document. In general,the document topic classification may not be exclusive. A candidatedocument may be classified as being a science document with probabilityof 0.8 and a business document with probability of 0.4. A link-basedprofile rank may be computed based on the relative weights allocated toa user's URL, host, domain, etc., preferences in the link-based profile.In one embodiment, term-based profile rank can be determined using knowntechniques, such as the term frequency-inverse document frequency(TF-IDF). The term frequency of a term is a function of the number oftimes the term appears in a document. The inverse document frequency isan inverse function of the number of documents in which the term appearswithin a collection of documents. For example, very common terms like“the” occur in many documents and consequently as assigned a relativelylow inverse document frequency.

When a search engine generates search results in response to a searchquery, a candidate document D that satisfies the query is assigned aquery score, QueryScore, in accordance with the search query. This queryscore is then modulated by document D's page rank, PageRank, to generatea generic score, GenericScore, that is expressed as

GenericScore=QueryScore*PageRank.

This generic score may not appropriately reflect document D's importanceto a particular user U if the user's interests or preferences aredramatically different from that of the random surfer. The relevance ofdocument D to user U can be accurately characterized by a set of profileranks, based on the correlation between document D's content and userU's term-based profile, herein called the TermScore, the correlationbetween one or more categories associated with document D and user U'scategory-based profile, herein called the CategoryScore, and thecorrelation between the URL and/or host of document D and user U'slink-based profile, herein called the LinkScore. Therefore, document Dmay be assigned a personalized rank that is a function of both thedocument's generic score and the user profile scores. In one embodiment,this personalized score can be expressed as:

PersonalizedScore=GenericScore*(TermScore+CategoryScore+LinkScore).

FIGS. 9A and 9B represent two embodiments, both implemented in aclient-server network environment such as the network environment 100shown in FIG. 1. In the embodiment shown in FIG. 9A, the search engine104 receives a search query from a client 102 at step 910 that issubmitted by a particular user. In response, the search engine 104 mayoptionally generate a query strategy at step 915 (e.g., the search queryis normalized so as to be in proper form for further processing, and/orthe search query may be modified in accordance with predefined criteriaso as to automatically broaden or narrow the scope of the search query).At step 920, the search engine 104 submits the search query (or thequery strategy, if one is generated) to the content server 106. Thecontent server identifies a list of documents that match the searchquery at step 920, each document having a generic score that depends onthe document's page rank and the search query. In general, all the threeoperations (steps 910, 915 and 920) are conducted by the search enginesystem 107, which is on the server side of the network environment 100.There are two options on where to implement the operations followingthese first three steps.

In some embodiments that employ a server-side implementation, the user'sidentification number is embedded in the search query. Based on theuser's identification number, the user profile server 108 identifies theuser's user profile at step 925. Starting from step 930, the userprofile server 108 or the search engine 104 analyzes each documentidentified at step 920 to determine its relevance to the user's profile,creates a profile score for the identified document at step 935 and thenassigns the document a personalized score that is a function of thedocument's generic and profile scores at step 940. At step 942, the userprofile server 108 or the search engine 104 checks whether this the lastone in the list of identified documents. If no, the system processes thenext document in the list. Otherwise, the list of documents arere-ordered according to their personalized scores at step 945 and thensent to the corresponding client from which the user submitted thesearch query.

Embodiments using a client-side implementation are similar to theserver-side implementation, except that after step 920, the identifieddocuments are sent to the corresponding client from which the usersubmitted the query. This client stores the user's user profile and itis responsible for re-ordering the documents based upon the userprofile. Therefore, this client-side implementation may reduce theserver's workload. Further, since there is no privacy concern with theclient-side implementation, a user may be more willing to provideprivate information to customize the search results. However, asignificant limitation to the client-side implementation is that only alimited number of documents, e.g., the top 50 documents (as determinedusing the generic rank), may be sent to a client for re-ordering due tolimited network bandwidth. In contrast, the server-side implementationmay be able to apply a user's profile to a much larger number ofdocuments, e.g., 1000, that match the search query. Therefore, theclient-side implementation may deprive a user access to those documentshaving relatively low generic ranks, but significantly high personalizedranks.

FIG. 9B illustrates another embodiment. Unlike the embodiment depictedin FIG. 9A, where the search query is not personalized before submittingthe search query to the search engine 104, a generic query strategy isadjusted (step 965) according to the user's user profile to create apersonalized query strategy. For example, relevant terms from the userprofile may be added to the search query with associated weights. Thecreation of the personalized query strategy can be performed either onthe client side or on the server side of the system. This embodimentavoids the network bandwidth restriction facing the previous embodiment.Finally, the search engine 104 submits the personalized query strategyto the content server 106 (step 970), and therefore the search resultsreturned by the content server have already been ordered by thedocuments' personalized ranks (step 975).

The profiles of a group of users with related interests may be combinedtogether to form a group profile, or a single profile may be formedbased on the documents identified by the users in the group. Forinstance, several family members may use the same computer to submitsearch queries to a search engine. If the computer is tagged with asingle user identifier by the search engine, the “user” will be theentire family of users, and the user profile will be represent acombination or mixture of the search preferences of the various familymembers. An individual user in the group may optionally have a separateuser profile that differentiates this user from other group members. Inoperation, the search results for a user in the group are rankedaccording to the group profile, or according to the group profile andthe user's user profile when the user also has a separate user profile.

It is possible that a user may switch his interests so dramatically thathis new interests and preferences bear little resemblance to his userprofile, or a user may be temporarily interested in a new topic. In thiscase, personalized search results produced according to the embodimentsdepicted in FIGS. 9A and 9B may be less favorable than search resultsranked in accordance with the generic ranks of the documents in thesearch results. Additionally, the search results provided to a user maynot include new websites among the top listed documents because theuser's profile tends to increase the weight of older websites which theuser has visited (i.e., older websites from which the user has viewed ordownloaded web pages) in the past.

To reduce the impact caused by a change in a user's preferences andinterests, the personalized search results may be merged with thegeneric search results. In one embodiment, the generic search resultsand personalized search results are interleaved, with the odd positions(e.g., 1, 3, 5, etc.) of a search results list reserved for genericsearch results and the even positions (e.g., 2, 4, 6, etc.) reserved forpersonalized search results, or vice versa. Preferably, the items in thegeneric search results will not duplicate the items listed in thepersonalized search results, and vice versa. More generally, genericsearch results are intermixed or interleaved with personalized searchresults, so that the items in the search results presented to the userinclude both generic and personalized search results.

In another embodiment, the personalized ranks and generic ranks arefurther weighted by a user profile's confidence level. The confidencelevel takes into account factors such as how much information has beenacquired about the user, how close the current search query matches theuser's profile, how old the user profile is, etc. If only a very shorthistory of the user is available, the user's profile may be assigned acorrespondingly low confidence value. The final score of an identifieddocument can be determined as:

FinalScore=ProfileScore*ProfileConfidence+GenericScore*(1−ProfileConfidence).

When intermixing generic and personalized results, the fraction ofpersonalized results may be adjusted based on the profile confidence,for example using only one personalized result when the confidence islow.

Sometimes, multiple users may share a machine, e.g., in a publiclibrary. These users may have different interests and preferences. Inone embodiment, a user may explicitly login to the service so the systemknows his identity. Alternatively, different users can be automaticallyrecognized based on the items they access or other characteristics oftheir access patterns. For example, different users may move the mousein different ways, type differently, and use different applications andfeatures of those applications. Based on a corpus of events on a clientand/or server, it is possible to create a model for identifying users,and for then using that identification to select an appropriate “user”profile. In such circumstances, the “user” may actually be a group ofpeople having somewhat similar computer usage patterns, interests andthe like.

Referring to FIG. 10, a personalized search engine system 1000 typicallyincludes one or more processing units (CPU's) 1002, one or more networkor other communications interfaces 1010, memory 1012, and one or morecommunication buses 1014 for interconnecting these components. Thesystem 1000 may optionally include a user interface 1004, for instance adisplay 1006 and a keyboard 1008. Memory 1012 may include high speedrandom access memory and may also include non-volatile memory, such asone or more magnetic disk storage devices. Memory 1012 may include massstorage that is remotely located from the central processing unit(s)1002. The memory 1012 preferably stores:

-   -   an operating system 1016 that includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 1018 that is used for connecting        the system 1000 to other servers or computers via one or more        communication networks (wired or wireless), such as the        Internet, other wide area networks, local area networks,        metropolitan area networks, and so on;    -   a system initialization module 1020 that initializes other        modules and data structures stored in memory 1012 required for        the appropriate operation of system 1000;    -   a search engine 1022 for processing a search query, identifying        and ordering search results according to the search query and a        user's profile;    -   a user profile engine 1030 for gathering and processing user        information, such as the user information identified in FIG. 2,        and creating and updating a user's user profile that        characterizes the user's search preferences and interests; and    -   data structures 1040, 1060 and 1080 for storing a plurality of        user profiles.

The search engine 1022 may further comprise:

-   -   a generic rank module (or instructions) 1024 for processing a        search query submitted by a user, identifying a list of        documents matching the query and assigning each identified        document a generic rank without reference to user specific        information;    -   a user profile rank module (or instructions) 1026 for        correlating each of a plurality of documents identified by the        generic rank module 1024 with the user's user profile and        assigning the document a profile rank indicating the relevance        of the document to the user's search preferences and interests;        and    -   a rank mixing module (or instructions) 1028 for combining the        generic rank and the profile rank of an identified document into        a personalized rank and re-ordering the list of documents        according to their personalized ranks.        In some embodiments, these modules 1024, 1026, 1028 may be        implemented within a single procedure or in a set of procedures        that reside within a single software module.

The user profile engine 1030 may further comprise:

-   -   a user information collection module 1032 for collecting and        assorting various user information listed in FIG. 2;    -   a document content extraction module 1034 for selecting and        extracting content from the documents identified by the user, to        identify content relevant to the user's interests, using        techniques such as paragraph sampling (as discussed above); and    -   a context analysis module 1036 for analyzing the content        extracted by the document extraction module 1034 so as to        identify terms that characterize a user's search preferences.

Each data structure hosting a user profile may further comprise:

-   -   a data structure 1042, 1062 or 1082 for storing a term-based        user profile;    -   a data structure 1044, 1064 or 1084 for storing a category-based        user profile; and    -   a data structure 1046, 1066 or 1086 for storing a link-based        user profile.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A computer-implemented method of personalizing search results of asearch engine, comprising: at a search engine system having a one ormore processors and memory storing programs executed by the one or moreprocessors: accessing a user profile for a user, wherein content of theuser profile is generated from user information that includesinformation derived from anchor text contained in documents that link todocuments accessed by the user; receiving a search query from the user;identifying a set of search result documents that match the searchquery; assigning a generic score to each document of at least a subsetof the set of search result documents; assigning a personalized score toeach document of the subset of search result documents in accordancewith the generic score assigned to the document and the user profile;ranking the subset of search result documents according to theirrespective personalized scores; providing the ranked subset of searchresult documents to a client system associated with the user; andupdating the user profile based on a document selected by the user fromthe ranked subset of search result documents.
 2. The method of claim 1,wherein the user information is derived from a first set of documentsthat includes: documents identified by search results from the searchengine, documents accessed by the user, documents linked to thedocuments identified by search results from the search engine, anddocuments linked to the documents accessed by the user.
 3. The method ofclaim 1, including updating the user profile by: updating a term-basedprofile of the user profile by identifying a set of terms from adocument in the first set of documents, and adding information about theidentified set of terms to the term-based profile.
 4. The method ofclaim 3, wherein a term in the term-based profile is an expressioncomprising at least one word and a weight.
 5. The method of claim 4,wherein the weight is a weight associated with occurrences of the termin the first set of documents.
 6. The method of claim 4, wherein theweight of a term depends at least partially on the term's term frequencyand inverse document frequency in said first set of documents.
 7. Themethod of claim 1, wherein the updating includes analyzing links withina document in the first set of documents and adding information derivedfrom the analyzed links to the user profile.
 8. The method of claim 7,wherein the information derived from the analyzed links that is added tothe user profile is added to a link-based profile and includesinformation about URLs or portions of URLs.
 9. The method of claim 8,wherein the link-based profile of the user profile comprises: aplurality of URLs and a weight associated with each URL, wherein theweight is based on one or more factors selected from the groupconsisting of frequency with which the user visits the URL, time theuser has spent viewing a document associated with the URL and quantityof the user's scrolling activity at the document; and a plurality ofhosts and a weight associated with each host, wherein the weight isbased on frequency of the user's visits to the host.
 10. The method ofclaim 9, wherein the URLs further include URLs that have not beenvisited by the user, but are related to the URLs that have been visitedby the user and the weight of an unvisited URL depends on its distanceto at least one related URLs that have been visited.
 11. The method ofclaim 1, wherein the updating includes updating a category-based profileof the user profile by classifying a document in the first set ofdocuments into a plurality of categories, and adding information aboutthe plurality of categories to the category-based profile.
 12. Themethod of claim 11, wherein a category in the category-based profilecharacterizes at least one aspect of documents in the category and thecategory is associated with a weight indicative of the category'simportance relative to other categories.
 13. The method of claim 12,wherein the at least one aspect of the documents in the category isselected from the group consisting of: document format, document type,document topic and document origin.
 14. A computer-implemented method ofpersonalizing search results of a search engine, comprising: creating aplurality of user profiles for a plurality of users, each user profileincluding at least a user's identification number and informationderived from documents visited by the user, including informationderived from anchor text contained in documents that link to thedocuments visited by the user; receiving a search query from a user ofthe plurality of users, the search query including at least one queryterm and the user's identification number; retrieving a user profilethat matches the user's identification number; generating a personalizedquery strategy from the search query and the user profile; selecting apersonalized set of documents from the Internet according to thepersonalized query strategy, each document having a generic rankingscore based at least in part on the relevance of the document to thesearch query; assigning to each document in the set a personalizedranking score based at least in part on the user profile and thedocument's generic ranking score; ranking the set of documents accordingto their generic and personalized ranking scores; providing the rankedset of search result documents to a client system associated with theuser; and updating the user profile of the user based on a documentselected by the user from the set of search result documents.
 15. Themethod of claim 14, wherein creating a user's user profile furthercomprises: creating a term-based profile by extracting a set of termsfrom documents visited by the user and associating a weight with eachextracted term; and creating a category-based profile by determining aplurality of categories associated with documents visited by the userand associating a weight with each determined category.
 16. The methodof claim 14, wherein creating a user's user profile further comprises:creating a link-based profile by analyzing links in documents visited bythe user and associating weights with the link.
 17. The method of claim14, wherein the user profile for a particular user includes demographicand geographic information provided by the user.
 18. The method of claim14, wherein the ranked set of documents comprises two subsets ofdocuments, one subset of documents ordered by their generic rankingscores and the other subset of documents ordered by personalized rankingscores.
 19. A search engine system, comprising: one or more centralprocessing units for executing programs; an interface for receivinginformation; and a search engine module executable by the one or morecentral processing units, the module comprising: instructions foraccessing a user profile for a user, wherein content of the user profileis generated from user information that includes information derivedfrom anchor text contained in documents that link to documents accessedby the user; instructions for receiving a search query from a user;instructions for identifying a set of search result documents that matchthe search query; instructions for assigning a generic score to eachdocument of at least a plurality of the search result documents;instructions for assigning personalized scores to each document of theplurality of search result documents in accordance with the genericscore assigned to the document and the user's user profile; instructionsfor ranking at least the plurality of the search result documentsaccording to personalized scores; instructions for providing the rankedset of search result documents to a client system associated with theuser; and instructions for updating the user profile based on a documentselected by the user from the set of search result documents.
 20. Thesystem of claim 19, wherein the user information is derived from a firstset of documents that includes: documents identified by search resultsfrom the search engine, documents accessed by the user, documents linkedto the documents identified by search results from the search engine,and documents linked to the documents accessed by the user.
 21. Thesystem of claim 19, wherein the information derived from the analyzedlinks that is added to the user profile is added to a link-based profileand includes information about URLs or portions of URLs.
 22. The systemof claim 21, wherein the link-based profile comprises: a plurality ofURLs and a weight associated with each URL, wherein the weight is basedon one or more factors selected from the group consisting of frequencywith which the user visits the URL, time the user has spent viewing adocument associated with the URL and quantity of the user's scrollingactivity at the document; and a plurality of hosts and a weightassociated with each host, wherein the weight is based on frequency ofthe user's visits to the host.
 23. The system of claim 22, wherein theURLs further include URLs that have not been visited by the user, butare related to the URLs that have been visited by the user and theweight of an unvisited URL depends on its distance to at least onerelated URLs that have been visited.
 24. The system of claim 19, furtherincluding: instructions for updating a term-based profile by identifyinga set of terms from a document in the set of documents, and addinginformation about the identified set of terms to the term-based profile;and instructions for updating a category-based profile by classifyingthe document into a plurality of categories, and adding informationabout the plurality of categories to the category-based profile.
 25. Thesystem of claim 24, wherein a term in the term-based profile is anexpression comprising at least one word and a weight.
 26. The system ofclaim 25, wherein the weight is a weight associated with occurrences ofthe term in the set of documents.
 27. The system of claim 25, whereinthe weight of a term depends at least partially on the term's termfrequency and inverse document frequency in said set of documents.
 28. Acomputer readable storage medium storing one or more programs forexecution by one or more processors, the one or more programscomprising: instructions for accessing a user profile for a user,wherein content of the user profile is generated from user informationthat includes information derived from anchor text contained indocuments that link to documents accessed by the user; instructions forreceiving a search query from a user; instructions for identifying a setof search result documents that match the search query; instructions forassigning a generic score to each document of at least a plurality ofthe search result documents; instructions for assigning personalizedscores to each document of the plurality of search result documents inaccordance with the generic score assigned to the document and theuser's user profile; instructions for ranking at least the plurality ofthe search result documents according to their personalized scores;instructions for providing the ranked set of search result documents toa client system associated with the user; and instructions for updatingthe user profile based on a document selected by the user from the setof search result documents.
 29. A computer-implemented method ofpersonalizing search results of a search engine, comprising: at a searchengine system having a one or more processors and memory storingprograms executed by the one or more processors: identifying a set ofdocuments accessed by a user; generating a user profile for the userthat includes terms selected by sampling the identified set ofdocuments, wherein sampling a document includes: excluding a set ofpredefined terms; excluding paragraphs whose lengths are less than apredefined minimum length; and limiting the number of terms selectedfrom each paragraph to a predefined maximum number; receiving a searchquery from the user; identifying a set of search result documents thatmatch the search query; assigning a generic score to each document of atleast a subset of the set of search result documents; assigning apersonalized score to each document of the subset of search resultdocuments in accordance with the generic score assigned to the documentand the user profile; ranking the subset of search result documentsaccording to their respective personalized scores; providing the rankedsubset of search result documents to a client system associated with theuser; and updating the user profile based on a document selected by theuser from the ranked subset of search result documents.
 30. Acomputer-implemented method of personalizing search results of a searchengine, comprising: at a search engine system having a one or moreprocessors and memory storing programs executed by the one or moreprocessors: identifying a plurality of context patterns from apredefined set of documents, wherein each respective context patterncomprises a respective variable term and one or more respective fixedterms, and the respective variable term together with the respectivefixed terms are in an identified ordered sequence; generating a userprofile for a user, wherein content of the user profile includes termsidentified by applying the plurality of context patterns to documentsaccessed by the user; receiving a search query from the user;identifying a set of search result documents that match the searchquery; assigning a generic score to each document of at least a subsetof the set of search result documents; assigning a personalized score toeach document of the subset of search result documents in accordancewith the generic score assigned to the document and the user profile;ranking the subset of search result documents according to theirrespective personalized scores; providing the ranked subset of searchresult documents to a client system associated with the user; andupdating the user profile based on a document selected by the user fromthe ranked subset of search result documents.